An important step in the manipulation of image data preparatory to invoking the recognition step is to subject the image to segmentation. If the image is one or more unknown handwritten characters, the segmentation process makes slices or cuts of the image thus to divide the image into its correct individual characters.
Because of the great variability of individual handwriting styles, prior art segmentation schemes which merely make a few straight cuts at the unknown image are ill-adapted to generate the "correct" cuts in a sufficiently high percentage of cases. At the other extreme, segmentation schemes which make many arbitrary cuts at an unknown string of handwritten script letters can create so large a number of sub-images (hereinafter, "cells") that the recognition engine is significantly overtaxed with having to make calculations. The result in the first case is an unacceptably low correct recognition rate, and in the second case it is a slowing of the speed of recognition.
An additional consideration in determining an optimum segmentation scheme for segmenting handwritten script is the presence of gray scale in much if not most real-life script. Grey scale further complicates making the segmentation cuts because the extent of grey in any given image can be subtle or on the other hand quite pronounced. Further, the interaction of grey scale with other attributes of script such as character overlap and slant is also very variable.